Many in the field are attempting to quantify images for various purposes but encounter issues relating to errors or unknown values in the imaging physics data. For example, trying to quantify the image based on an absolute model of the physics along with assumed properties of tissue needs all the imaging physics data to be known and accurate. Even the inclusion of a quantification step wedge into each image still requires much of the imaging physics data and assumes it is accurate. Hence the need to use in-image reference values.
For example, a mammogram is created by sending x-ray photons towards the breast and then detecting how many x-ray photons make it through. The smaller the number of x-ray photons that make it through, the denser the breast tissue.
Breast density has been linked by many studies to likelihood of developing breast cancer and most of those studies have assessed breast density either using visual or semi-automated methods. A general overview is given, for example, in Breast Cancer Research's review series, including Vachon et al, “Mammographic density, breast cancer risk and risk prediction” (Breast Cancer Research, 2007, vol 9:217), Martin and Boyd, “Potential mechanisms of breast cancer risk associated with mammographic density” (Breast Cancer Research, 2008, Vol 10:201) and Yaffe, “Measurement of mammographic density” (Breast Cancer Research, 2008, Vol 10:209).